Capability two
Software and governed AI
Artificial intelligence creates durable value only when it is governed, when it runs where the data is allowed to live, and when it produces a decision rather than a summary. We build to all three.
IBM Business Partner and TD Synnex partner
watsonx services, products, and hosting
We license, architect, deploy, and operate the watsonx portfolio. That covers the AI lifecycle studio, model development, natural language processing, model governance, and the data layer beneath it. We deliver the platform, we build the models, and where the client prefers, we host and operate the whole thing under a fixed monthly arrangement.
- Licensing and procurement through established partner channels
- Architecture and deployment across on premises, hybrid, and FedRAMP Moderate cloud environments
- Model development by our own data science team, including your models, open source models, and our accelerators
- Governance covering bias control, model lineage, approval workflow, and audit evidence
- Managed operation with all inclusive support so you do not staff a platform team
On premises AI stack appliances
A governed AI stack that arrives as a rack.
The platform layer is Red Hat OpenShift, acceleration is GPU based and sized to the workload, the AI layer is governed, and the unit arrives installed, integrated, and supported. It answers the case where an organization needs serious AI capacity but cannot grow a data center, cannot move regulated data to public cloud, or cannot accept its current cost curve.
Exit the hyperscaler trap
Steady state inference at metered cloud rates is now a visible line on the income statement. Moving predictable workloads onto owned hardware changes the shape of that curve, and a hybrid control plane keeps the cloud available for burst.
Avoid data center growth
Rack scale capacity absorbs the AI workload without a facility expansion program, a new power study, or a capital request that takes two budget cycles to clear.
Keep data inside the boundary
Sovereignty rules, controlled unclassified information handling, and customer contractual terms are pushing data back on premises. The appliance is built for that from the start rather than retrofitted.
Deployment options
We are not committed to one delivery model. We are committed to the one that matches your data, your facility, and your cost structure.
| Option | Best fit | What we provide |
|---|---|---|
| On premises appliance | Regulated data, capped facility, predictable workload | Hardware, OpenShift, governed AI layer, install and support |
| Certified hosted | No facility appetite, still needs control and CUI handling | Certified data center, operations, monthly all inclusive pricing |
| Hybrid control plane | Existing cloud estate plus a new sovereign requirement | Architecture, migration, workload placement policy, governance |
| FedRAMP Moderate cloud | Federal programs needing accredited commercial cloud | watsonx deployment, authorization support, model governance |
Decision optimization and prescriptive AI
Prediction alone rarely changes an outcome. We pair deep learning models that score risk and forecast condition with constrained optimization that produces an executable plan, plus a rules layer enforcing the constraints a model must never violate.
In practice that is a maintenance schedule respecting crew capacity and regulatory intervals, or a supply plan respecting lead time and cash. Large language models serve the edges of this work for explanation and interaction, and we deliberately keep them out of the decision path.
Software assurance and open source risk
Delivered software carries liability, and open source components are the dominant source of that exposure. We provide software bill of materials management, continuous vulnerability monitoring, and cybersecurity modeling using system of systems and model based methods.
The result is that security posture becomes part of the architecture rather than a report produced after the fact, which is the only version an acquiring authority or a downstream customer will actually accept.
Start with the smallest piece of work that proves us out.
A fixed price study, a working prototype, or one embedded expert. We scope the first step so the deliverable is useful even if you never hire us again.
We will bring an engineer, not a salesperson.